The commonly used policy representations for learning systems include radial basis function networks [1]}, dynamical movement primitives [2]}, [3]} and feed-forward neural networks [1]}, [5]}. In recent years deep artificial neural networks (ANN) seem to become a popular policy.
All of them have in common that their final representation can be difficult to interpret. Even if a policy only sets a target pose for the robot to reach, it can be problematic to know how it reacts in all parts of the state space. In contrast to that, [6]} suggests to learn interpretable policies based on behavior trees [7]} that work explicitly in end-effector space.
